Why does the binary cross-entropy work better than categorical cross-entropy in a multi-class single label problem?
https://stats.stackexchange.com/questions/260505/machine-learning-should-i-use-a-categorical-cross-entropy-or-binary-cross-entro
Is relevant.

based on my reading when you have a NN and do Binary crossentropy on what you might call 'Linked category data' the accuracy can tend to be better than in a Categorical crossentropy model. The binary aspect implies the categories can undergo multiple splits before deciding on the exact category when the data is categorically splittable like this in a tree like hierarchy the accuracy can tend to be better.

Think of how difficult it would be to memorize the name of every type of clothing in someones wardrobe if each piece had its own special name. Vs if they had structurally relevant names like Upper/lower for category number one is it warn on your top half or bottom half. followed by inner or outer. It an inner or outer layer of clothing. learning such binary name/feature categories enables a more accurate model. If it was data unrelated in this way it would most likely not be as accurate. The binary model can take advantage learning such features while a muti categorical model I think assumes independence and tries to learn best the features of each group and gives out a prediction of how sure it falls in each category.